Urban planning for wildlife connectivity: A multispecies assessment of urban sprawl and SLOSS renaturalization strategies
Bibliographic record
Abstract
Abstract Urban areas are under constant pressure to accommodate more people, leading to an increase in built surface, further reducing and fragmenting wildlife habitat areas within and around cities. Nonetheless, wildlife populations may persist by using a network of habitat fragments, such as urban green areas, but only if functionally interconnected. Therefore, a proactive approach considering prospective landscape connectivity changes, following proposed developments, and potential mitigation strategies is needed. We predicted present‐day and prospective landscape connectivity for six taxa, in the Greater Toronto Area, under landscape changes across three future scenarios which include the conversion of agriculture to developed land with (1) no mitigation strategies, (2) renaturalization of single‐large areas and (3) renaturalization of small and widespread areas. We used Omniscape to identify shared movement corridors across taxa, and Graphab to estimate the importance of each habitat patch for the overall connectivity ( IIC k ), the isolation degree of habitat patches (node degree) and the distribution of stepping‐stone pressure (betweenness centrality). We validated our connectivity assessment through a road mortality risk assessment near the predicted present‐day movement corridors. Without mitigation strategies, the proposed developments will increase the isolation of currently near‐isolated patches and the importance of nearby patches as stepping‐stones. There will be a shift in the distribution of movement corridors to remnant green areas within newly developed land, and the fragmentation of three key corridors connecting peripheral areas to inner‐city forested areas. Mitigation strategies with small and widespread renaturalized green areas provided the best outcome in terms of connectivity and can compensate habitat loss following new developments. The validation analysis supported our connectivity modelling assessment and revealed that road mortality risk was elevated near roads intersecting movement corridors. This finding strongly indicates that roads act as barriers to connectivity and should be explicitly addressed in wildlife connectivity strategies, such as natural heritage or greenspace planning. Synthesis and applications . Notably, smaller but widely distributed natural areas can serve as a practical and effective management strategy for balancing the trade‐offs among economic costs, urban expansion and habitat conservation. To effectively support biodiversity conservation goals, such as the 30 × 30 target, these smaller natural areas should ideally be connected to nearby larger natural areas through natural corridors or mitigation infrastructure.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".